基于尺度-空间极值的织物起球目标检测

针对织物起球等级客观评估中的起球特征提取问题，提出了一种基于尺度-空间极值的毛球目标检测算法。首先建立各向异性高斯毛球目标模型，然后应用高斯尺度空间理论及精简的各向同性毛球目标模型，构造与该目标模型最优匹配算子近似的高斯偏导多项式作为检测算子，以其匹配能量输出作为尺度选择依据，并根据尺度-空间极值原则，定位并确定毛球目标的各向同性尺度，再基于此尺度下的局部结构张量矩阵估计各向异性高斯模型参数，最后根据该模型对毛球进行局部分割和识别。实验结果表明该算法能够较好地识别和分割毛球。

Fabric pilling object detection using scale-space extrema

Focusing on objective assessment of fabric pilling grading, a method was proposed for detecting pilling object using scale-space extrema. The pilling object was modeled as an anisotropic Gaussian kernel. Based on scale-space theory and derivation of isotropic Gaussian matched filter, an operator as polynomial combinations of Gaussian derivatives was used for automatic scale selection, which providing a close approximation to Gaussian matched filter. By scale-space extrema of the normalized operator filtering , the pilling object was located and its size was measured. The anisotropic Gaussian model parameters were estimated from local structure tensor matrix, and depending on them, the pilling object was finally segmented and recognized. The experiments demonstrated that the presented method of pilling object segmentation and recognition has good results.